U.S. Department of Energy

Pacific Northwest National Laboratory

A multi-omic systems approach to elucidating Yersinia virulence mechanisms.

TitleA multi-omic systems approach to elucidating Yersinia virulence mechanisms.
Publication TypeJournal Article
Year of Publication2013
AuthorsAnsong C, Schrimpe-Rutledge AC, Mitchell HD, Chauhan S, Jones MB, Kim Y-M, McAteer K, Kaiser BLDeathera, Dubois JL, Brewer HM, Frank BC, McDermott JE, Metz TO, Peterson SN, Smith RD, Motin VL, Adkins JN
JournalMol Biosyst
KeywordsAnimals, Body Temperature, Cluster Analysis, Gene Expression Profiling, Glutamic Acid, Host-Pathogen Interactions, Mammals, Models, Biological, Proteomics, Siphonaptera, Transcriptome, Virulence, Yersinia

The underlying mechanisms that lead to dramatic differences between closely related pathogens are not always readily apparent. For example, the genomes of Yersinia pestis (YP) the causative agent of plague with a high mortality rate and Yersinia pseudotuberculosis (YPT) an enteric pathogen with a modest mortality rate are highly similar with some species specific differences; however the molecular causes of their distinct clinical outcomes remain poorly understood. In this study, a temporal multi-omic analysis of YP and YPT at physiologically relevant temperatures was performed to gain insights into how an acute and highly lethal bacterial pathogen, YP, differs from its less virulent progenitor, YPT. This analysis revealed higher gene and protein expression levels of conserved major virulence factors in YP relative to YPT, including the Yop virulon and the pH6 antigen. This suggests that adaptation in the regulatory architecture, in addition to the presence of unique genetic material, may contribute to the increased pathogenecity of YP relative to YPT. Additionally, global transcriptome and proteome responses of YP and YPT revealed conserved post-transcriptional control of metabolism and the translational machinery including the modulation of glutamate levels in Yersiniae. Finally, the omics data was coupled with a computational network analysis, allowing an efficient prediction of novel Yersinia virulence factors based on gene and protein expression patterns.

Alternate JournalMol Biosyst
PubMed ID23147219
PubMed Central IDPMC3518462
Grant List5P41RR018522-10 / RR / NCRR NIH HHS / United States
8 P41 GM103493-10 / GM / NIGMS NIH HHS / United States
P41 GM103493 / GM / NIGMS NIH HHS / United States
Y1-AI-8401 / AI / NIAID NIH HHS / United States
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